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Activity Number:
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600
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Type:
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Contributed
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Date/Time:
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Thursday, August 6, 2009 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics in Epidemiology
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| Abstract - #303324 |
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Title:
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Joint Variable Selection of Fixed and Random Effects in a Linear Mixed-Effects Model and Its Oracle Properties
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Author(s):
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Arun Krishna*+ and Howard D. Bondell and Sujit Ghosh
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Companies:
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North Carolina State University and North Carolina State University and North Carolina State University
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Address:
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4951 Tall Timber Dr, Raleigh, NC, 27612,
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Keywords:
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Adaptive LASSO ; Modified Cholesky Decomposition ; Constrained EM Algorithm ; Penalized Likelihood
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Abstract:
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We propose a new method of simultaneously identifying the important predictors that correspond to both the fixed and random effects components in a linear mixed-effects model. A reparameterized version of the linear mixed-effects model using a modified Cholesky decomposition is proposed to aid in the selection by dropping out the random effect terms whose corresponding variance is set to zero. We propose a penalized joint log-likelihood procedure with an adaptive penalty for the selection and estimation of the fixed and random effects. A constrained EM algorithm is then used to obtain the final estimates. We further show that our penalized estimator enjoys the Oracle property, in that, asymptotically it performs as well as if the true model was known beforehand. We demonstrate the performance of our method based on a simulation study and a real data example.
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